Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Downloading mnist: 9.92MB [00:03, 3.02MB/s]                            
Extracting mnist: 100%|██████████| 60.0K/60.0K [00:10<00:00, 5.59KFile/s] 
Downloading celeba: 1.44GB [02:56, 8.17MB/s]                               
Extracting celeba...

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f7d149dd4e0>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f7d2c8724a8>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    input_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')
    input_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    learning_rate = tf.placeholder(tf.float32, name='learning_rate')
    
    return input_real, input_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the generator, tensor logits of the generator).

In [6]:
def discriminator(images, reuse=False, alpha=0.2):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :param alpha: Leaky ReLU factor (additionally added)
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    with tf.variable_scope('discriminator', reuse=reuse):       
        # Image size is 28x28x3
        x1 = tf.layers.conv2d(images, filters=64, kernel_size=5, strides=2, padding='same')
        x1 = tf.maximum(x1, x1 * alpha)
        
        # Image size is 14x14x64
        x2 = tf.layers.conv2d(x1, filters=128, kernel_size=5, strides=2, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=True)
        x2 = tf.maximum(x2, x2 * alpha)
        
        # Image size is 8x8x128
        x3 = tf.layers.conv2d(x2, filters=256, kernel_size=5, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=True)
        x3 = tf.maximum(x3, x3 * alpha)
        
        flat = tf.reshape(x3, (-1, 8*8*128))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
    
    return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True, alpha=0.2):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :param alpha: Leaky ReLU (added)
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    with tf.variable_scope('generator', reuse=not is_train):
        x1 = tf.layers.dense(z, 7*7*512, activation=None) 
        x1 = tf.reshape(x1, (-1, 7, 7, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(x1, x1 * alpha)
        # 7x7x256
        
        x2 = tf.layers.conv2d_transpose(x1, filters=256, kernel_size=5, strides=2, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(x2, x2 * alpha)
        # 14x14x256
        
        x3 = tf.layers.conv2d_transpose(x2, filters=128, kernel_size=5, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(x3, x3 * alpha)
        # 28x28x128
        
        # Output layer 
        logits = tf.layers.conv2d_transpose(x3, filters=out_channel_dim, kernel_size=5, strides=1, padding='same')
        # 28 x 28 x outchannel_dim
        out = tf.tanh(logits)
    
    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
    
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    
    # TODO: Build Model with previous functions
    
    # Reset everything
    #tf.reset_default_graph()
    
    # Generate model
    input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3]) #mimic input channels
    d_train_opt, g_train_opt= model_opt(d_loss, g_loss, learning_rate, beta1)
    
    # Verbose variable
    steps = 0
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps +=1
                
                # Scale images
                batch_images *=2
                
                # Sample random noise for generator
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                # Run Optimizzzzzzzers
                _ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z})
                _ = sess.run(g_train_opt, feed_dict={input_real: batch_images, input_z: batch_z}) # needs images?
                
                # Verbose output
                if steps % 10 == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    
                if steps % 50 == 0:
                    show_generator_output(sess, 20, input_z, data_shape[3], data_image_mode)

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [14]:
batch_size = 64
z_dim = 100
learning_rate = 0.001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 0.0578... Generator Loss: 11.3536
Epoch 1/2... Discriminator Loss: 0.1069... Generator Loss: 7.5171
Epoch 1/2... Discriminator Loss: 0.0144... Generator Loss: 4.7003
Epoch 1/2... Discriminator Loss: 4.3953... Generator Loss: 0.0446
Epoch 1/2... Discriminator Loss: 3.0776... Generator Loss: 0.7454
Epoch 1/2... Discriminator Loss: 2.9104... Generator Loss: 0.2103
Epoch 1/2... Discriminator Loss: 1.6918... Generator Loss: 0.6357
Epoch 1/2... Discriminator Loss: 0.9429... Generator Loss: 0.7405
Epoch 1/2... Discriminator Loss: 0.9913... Generator Loss: 2.0120
Epoch 1/2... Discriminator Loss: 0.8416... Generator Loss: 1.6770
Epoch 1/2... Discriminator Loss: 0.8107... Generator Loss: 1.0124
Epoch 1/2... Discriminator Loss: 1.0661... Generator Loss: 1.0657
Epoch 1/2... Discriminator Loss: 1.3451... Generator Loss: 1.4975
Epoch 1/2... Discriminator Loss: 1.1294... Generator Loss: 0.7992
Epoch 1/2... Discriminator Loss: 1.3281... Generator Loss: 0.8061
Epoch 1/2... Discriminator Loss: 1.1900... Generator Loss: 0.6672
Epoch 1/2... Discriminator Loss: 1.2375... Generator Loss: 0.6984
Epoch 1/2... Discriminator Loss: 1.0643... Generator Loss: 1.0239
Epoch 1/2... Discriminator Loss: 1.5724... Generator Loss: 0.6229
Epoch 1/2... Discriminator Loss: 1.2624... Generator Loss: 0.6234
Epoch 1/2... Discriminator Loss: 0.9239... Generator Loss: 0.9201
Epoch 1/2... Discriminator Loss: 1.4826... Generator Loss: 0.5367
Epoch 1/2... Discriminator Loss: 1.5358... Generator Loss: 0.4424
Epoch 1/2... Discriminator Loss: 1.7741... Generator Loss: 0.3141
Epoch 1/2... Discriminator Loss: 1.2821... Generator Loss: 0.6940
Epoch 1/2... Discriminator Loss: 0.9899... Generator Loss: 0.9469
Epoch 1/2... Discriminator Loss: 1.3337... Generator Loss: 0.6796
Epoch 1/2... Discriminator Loss: 1.5550... Generator Loss: 0.4303
Epoch 1/2... Discriminator Loss: 1.2207... Generator Loss: 0.5004
Epoch 1/2... Discriminator Loss: 1.0800... Generator Loss: 1.7544
Epoch 1/2... Discriminator Loss: 1.2432... Generator Loss: 0.6091
Epoch 1/2... Discriminator Loss: 1.2966... Generator Loss: 0.7984
Epoch 1/2... Discriminator Loss: 1.0214... Generator Loss: 0.9140
Epoch 1/2... Discriminator Loss: 1.1892... Generator Loss: 0.7147
Epoch 1/2... Discriminator Loss: 1.1225... Generator Loss: 1.7395
Epoch 1/2... Discriminator Loss: 1.2813... Generator Loss: 0.8322
Epoch 1/2... Discriminator Loss: 1.4735... Generator Loss: 0.5246
Epoch 1/2... Discriminator Loss: 0.9327... Generator Loss: 1.4474
Epoch 1/2... Discriminator Loss: 1.0837... Generator Loss: 1.2022
Epoch 1/2... Discriminator Loss: 1.4353... Generator Loss: 0.5653
Epoch 1/2... Discriminator Loss: 1.0522... Generator Loss: 0.7431
Epoch 1/2... Discriminator Loss: 1.2219... Generator Loss: 1.0906
Epoch 1/2... Discriminator Loss: 1.3109... Generator Loss: 1.4893
Epoch 1/2... Discriminator Loss: 1.1791... Generator Loss: 0.9389
Epoch 1/2... Discriminator Loss: 0.8054... Generator Loss: 1.0220
Epoch 1/2... Discriminator Loss: 1.0126... Generator Loss: 1.0615
Epoch 1/2... Discriminator Loss: 1.1656... Generator Loss: 0.6152
Epoch 1/2... Discriminator Loss: 1.1195... Generator Loss: 0.6977
Epoch 1/2... Discriminator Loss: 0.9449... Generator Loss: 0.9098
Epoch 1/2... Discriminator Loss: 1.2268... Generator Loss: 0.5475
Epoch 1/2... Discriminator Loss: 1.1303... Generator Loss: 1.2884
Epoch 1/2... Discriminator Loss: 1.0861... Generator Loss: 0.7453
Epoch 1/2... Discriminator Loss: 1.2735... Generator Loss: 1.8052
Epoch 1/2... Discriminator Loss: 1.1214... Generator Loss: 0.6181
Epoch 1/2... Discriminator Loss: 1.0578... Generator Loss: 1.1407
Epoch 1/2... Discriminator Loss: 1.4088... Generator Loss: 1.4344
Epoch 1/2... Discriminator Loss: 1.7309... Generator Loss: 0.2953
Epoch 1/2... Discriminator Loss: 1.0494... Generator Loss: 0.7989
Epoch 1/2... Discriminator Loss: 1.1159... Generator Loss: 0.7704
Epoch 1/2... Discriminator Loss: 1.3499... Generator Loss: 0.3832
Epoch 1/2... Discriminator Loss: 0.9658... Generator Loss: 1.2118
Epoch 1/2... Discriminator Loss: 1.1182... Generator Loss: 1.1677
Epoch 1/2... Discriminator Loss: 1.0633... Generator Loss: 1.0962
Epoch 1/2... Discriminator Loss: 1.0118... Generator Loss: 0.7796
Epoch 1/2... Discriminator Loss: 1.0248... Generator Loss: 1.4172
Epoch 1/2... Discriminator Loss: 1.2854... Generator Loss: 0.4575
Epoch 1/2... Discriminator Loss: 1.4030... Generator Loss: 0.6588
Epoch 1/2... Discriminator Loss: 1.6077... Generator Loss: 0.3103
Epoch 1/2... Discriminator Loss: 0.9591... Generator Loss: 1.2576
Epoch 1/2... Discriminator Loss: 1.2170... Generator Loss: 1.8035
Epoch 1/2... Discriminator Loss: 1.4178... Generator Loss: 0.5015
Epoch 1/2... Discriminator Loss: 1.5618... Generator Loss: 0.3506
Epoch 1/2... Discriminator Loss: 1.1592... Generator Loss: 1.9304
Epoch 1/2... Discriminator Loss: 1.0368... Generator Loss: 2.1108
Epoch 1/2... Discriminator Loss: 1.3013... Generator Loss: 0.4379
Epoch 1/2... Discriminator Loss: 1.1953... Generator Loss: 0.5760
Epoch 1/2... Discriminator Loss: 0.9320... Generator Loss: 0.9392
Epoch 1/2... Discriminator Loss: 1.4469... Generator Loss: 0.3814
Epoch 1/2... Discriminator Loss: 1.1021... Generator Loss: 0.6988
Epoch 1/2... Discriminator Loss: 1.0954... Generator Loss: 1.5372
Epoch 1/2... Discriminator Loss: 0.9468... Generator Loss: 1.1619
Epoch 1/2... Discriminator Loss: 0.8278... Generator Loss: 1.3769
Epoch 1/2... Discriminator Loss: 0.9324... Generator Loss: 0.8634
Epoch 1/2... Discriminator Loss: 0.8371... Generator Loss: 0.8371
Epoch 1/2... Discriminator Loss: 0.9434... Generator Loss: 0.8570
Epoch 1/2... Discriminator Loss: 1.4741... Generator Loss: 0.5429
Epoch 1/2... Discriminator Loss: 1.4609... Generator Loss: 1.1281
Epoch 1/2... Discriminator Loss: 1.1134... Generator Loss: 1.0700
Epoch 1/2... Discriminator Loss: 1.1584... Generator Loss: 0.5623
Epoch 1/2... Discriminator Loss: 1.0001... Generator Loss: 0.7949
Epoch 1/2... Discriminator Loss: 1.4541... Generator Loss: 0.3932
Epoch 1/2... Discriminator Loss: 0.9595... Generator Loss: 0.8646
Epoch 1/2... Discriminator Loss: 0.9370... Generator Loss: 0.7614
Epoch 2/2... Discriminator Loss: 0.8692... Generator Loss: 1.0200
Epoch 2/2... Discriminator Loss: 0.9165... Generator Loss: 1.6252
Epoch 2/2... Discriminator Loss: 1.7820... Generator Loss: 0.2538
Epoch 2/2... Discriminator Loss: 1.0780... Generator Loss: 1.3853
Epoch 2/2... Discriminator Loss: 0.9765... Generator Loss: 1.0461
Epoch 2/2... Discriminator Loss: 1.4706... Generator Loss: 0.3778
Epoch 2/2... Discriminator Loss: 1.0446... Generator Loss: 0.7827
Epoch 2/2... Discriminator Loss: 1.2597... Generator Loss: 0.7147
Epoch 2/2... Discriminator Loss: 0.9518... Generator Loss: 0.7921
Epoch 2/2... Discriminator Loss: 0.8084... Generator Loss: 0.9207
Epoch 2/2... Discriminator Loss: 1.1509... Generator Loss: 1.2935
Epoch 2/2... Discriminator Loss: 1.0592... Generator Loss: 0.9079
Epoch 2/2... Discriminator Loss: 1.2240... Generator Loss: 0.4943
Epoch 2/2... Discriminator Loss: 1.4096... Generator Loss: 0.3728
Epoch 2/2... Discriminator Loss: 1.0628... Generator Loss: 0.6247
Epoch 2/2... Discriminator Loss: 1.0284... Generator Loss: 0.7009
Epoch 2/2... Discriminator Loss: 2.1034... Generator Loss: 0.1886
Epoch 2/2... Discriminator Loss: 0.7627... Generator Loss: 0.9767
Epoch 2/2... Discriminator Loss: 0.5849... Generator Loss: 1.4117
Epoch 2/2... Discriminator Loss: 0.8125... Generator Loss: 1.1476
Epoch 2/2... Discriminator Loss: 1.2171... Generator Loss: 2.4181
Epoch 2/2... Discriminator Loss: 1.5501... Generator Loss: 0.4530
Epoch 2/2... Discriminator Loss: 1.1457... Generator Loss: 0.6100
Epoch 2/2... Discriminator Loss: 1.1639... Generator Loss: 0.5651
Epoch 2/2... Discriminator Loss: 0.7938... Generator Loss: 0.8380
Epoch 2/2... Discriminator Loss: 1.0230... Generator Loss: 1.2599
Epoch 2/2... Discriminator Loss: 1.1174... Generator Loss: 0.5715
Epoch 2/2... Discriminator Loss: 0.7220... Generator Loss: 1.1127
Epoch 2/2... Discriminator Loss: 1.1322... Generator Loss: 0.8966
Epoch 2/2... Discriminator Loss: 1.0924... Generator Loss: 1.5262
Epoch 2/2... Discriminator Loss: 1.1501... Generator Loss: 0.5561
Epoch 2/2... Discriminator Loss: 1.6164... Generator Loss: 1.8826
Epoch 2/2... Discriminator Loss: 1.1150... Generator Loss: 0.7383
Epoch 2/2... Discriminator Loss: 1.5346... Generator Loss: 0.3519
Epoch 2/2... Discriminator Loss: 1.7876... Generator Loss: 0.2457
Epoch 2/2... Discriminator Loss: 0.7135... Generator Loss: 1.7728
Epoch 2/2... Discriminator Loss: 2.3613... Generator Loss: 4.2880
Epoch 2/2... Discriminator Loss: 1.4420... Generator Loss: 0.4319
Epoch 2/2... Discriminator Loss: 1.1942... Generator Loss: 1.0425
Epoch 2/2... Discriminator Loss: 0.9419... Generator Loss: 0.8469
Epoch 2/2... Discriminator Loss: 0.8741... Generator Loss: 1.2388
Epoch 2/2... Discriminator Loss: 2.6645... Generator Loss: 0.0947
Epoch 2/2... Discriminator Loss: 1.0855... Generator Loss: 1.4038
Epoch 2/2... Discriminator Loss: 0.9902... Generator Loss: 1.5017
Epoch 2/2... Discriminator Loss: 1.0455... Generator Loss: 0.5861
Epoch 2/2... Discriminator Loss: 0.7952... Generator Loss: 1.4278
Epoch 2/2... Discriminator Loss: 2.4525... Generator Loss: 3.3600
Epoch 2/2... Discriminator Loss: 1.5900... Generator Loss: 0.3639
Epoch 2/2... Discriminator Loss: 0.6968... Generator Loss: 1.4129
Epoch 2/2... Discriminator Loss: 1.0462... Generator Loss: 1.0343
Epoch 2/2... Discriminator Loss: 0.6321... Generator Loss: 1.3139
Epoch 2/2... Discriminator Loss: 0.7635... Generator Loss: 1.9340
Epoch 2/2... Discriminator Loss: 0.9875... Generator Loss: 0.8441
Epoch 2/2... Discriminator Loss: 0.6599... Generator Loss: 1.4113
Epoch 2/2... Discriminator Loss: 0.8807... Generator Loss: 1.5916
Epoch 2/2... Discriminator Loss: 0.7768... Generator Loss: 1.4391
Epoch 2/2... Discriminator Loss: 0.5847... Generator Loss: 1.8415
Epoch 2/2... Discriminator Loss: 0.8795... Generator Loss: 1.2591
Epoch 2/2... Discriminator Loss: 0.8771... Generator Loss: 0.8300
Epoch 2/2... Discriminator Loss: 0.4586... Generator Loss: 1.9583
Epoch 2/2... Discriminator Loss: 0.7613... Generator Loss: 1.1038
Epoch 2/2... Discriminator Loss: 0.8154... Generator Loss: 1.0527
Epoch 2/2... Discriminator Loss: 1.0951... Generator Loss: 1.9961
Epoch 2/2... Discriminator Loss: 1.1244... Generator Loss: 1.7254
Epoch 2/2... Discriminator Loss: 1.2843... Generator Loss: 0.4539
Epoch 2/2... Discriminator Loss: 1.3914... Generator Loss: 1.7009
Epoch 2/2... Discriminator Loss: 0.6171... Generator Loss: 1.3450
Epoch 2/2... Discriminator Loss: 1.5604... Generator Loss: 0.4086
Epoch 2/2... Discriminator Loss: 1.2371... Generator Loss: 0.6009
Epoch 2/2... Discriminator Loss: 0.8100... Generator Loss: 1.2817
Epoch 2/2... Discriminator Loss: 0.7574... Generator Loss: 1.0035
Epoch 2/2... Discriminator Loss: 1.5058... Generator Loss: 0.4940
Epoch 2/2... Discriminator Loss: 0.6309... Generator Loss: 1.1366
Epoch 2/2... Discriminator Loss: 0.6061... Generator Loss: 1.0775
Epoch 2/2... Discriminator Loss: 1.3228... Generator Loss: 2.6884
Epoch 2/2... Discriminator Loss: 1.2480... Generator Loss: 0.5077
Epoch 2/2... Discriminator Loss: 1.2295... Generator Loss: 0.5135
Epoch 2/2... Discriminator Loss: 0.9496... Generator Loss: 0.8719
Epoch 2/2... Discriminator Loss: 0.9308... Generator Loss: 0.8660
Epoch 2/2... Discriminator Loss: 1.3546... Generator Loss: 0.4383
Epoch 2/2... Discriminator Loss: 0.9838... Generator Loss: 1.3971
Epoch 2/2... Discriminator Loss: 0.7685... Generator Loss: 2.1137
Epoch 2/2... Discriminator Loss: 0.9273... Generator Loss: 1.4254
Epoch 2/2... Discriminator Loss: 0.6222... Generator Loss: 1.2492
Epoch 2/2... Discriminator Loss: 1.2169... Generator Loss: 0.5735
Epoch 2/2... Discriminator Loss: 1.2053... Generator Loss: 0.5704
Epoch 2/2... Discriminator Loss: 1.6610... Generator Loss: 0.3768
Epoch 2/2... Discriminator Loss: 1.3269... Generator Loss: 0.5140
Epoch 2/2... Discriminator Loss: 1.5174... Generator Loss: 0.3783
Epoch 2/2... Discriminator Loss: 0.8761... Generator Loss: 0.8161
Epoch 2/2... Discriminator Loss: 0.9939... Generator Loss: 1.0551
Epoch 2/2... Discriminator Loss: 0.9964... Generator Loss: 1.0553
Epoch 2/2... Discriminator Loss: 0.7290... Generator Loss: 1.2090
Epoch 2/2... Discriminator Loss: 0.4923... Generator Loss: 1.5308

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [12]:
batch_size = 128
z_dim = 100
learning_rate = 0.0005
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 10

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/10... Discriminator Loss: 0.0302... Generator Loss: 4.0288
Epoch 1/10... Discriminator Loss: 0.1809... Generator Loss: 24.6454
Epoch 1/10... Discriminator Loss: 0.3485... Generator Loss: 2.7163
Epoch 1/10... Discriminator Loss: 4.5386... Generator Loss: 12.7741
Epoch 1/10... Discriminator Loss: 1.7907... Generator Loss: 0.4972
Epoch 1/10... Discriminator Loss: 0.3732... Generator Loss: 3.1758
Epoch 1/10... Discriminator Loss: 0.9025... Generator Loss: 0.9646
Epoch 1/10... Discriminator Loss: 0.8628... Generator Loss: 6.1097
Epoch 1/10... Discriminator Loss: 1.1994... Generator Loss: 0.5395
Epoch 1/10... Discriminator Loss: 0.7696... Generator Loss: 9.8692
Epoch 1/10... Discriminator Loss: 0.1740... Generator Loss: 2.3754
Epoch 1/10... Discriminator Loss: 1.9310... Generator Loss: 7.1852
Epoch 1/10... Discriminator Loss: 3.3081... Generator Loss: 0.0677
Epoch 1/10... Discriminator Loss: 0.9727... Generator Loss: 0.8662
Epoch 1/10... Discriminator Loss: 2.9655... Generator Loss: 0.1066
Epoch 1/10... Discriminator Loss: 1.1353... Generator Loss: 3.2655
Epoch 1/10... Discriminator Loss: 1.2409... Generator Loss: 1.3108
Epoch 1/10... Discriminator Loss: 0.8996... Generator Loss: 0.9706
Epoch 1/10... Discriminator Loss: 1.1497... Generator Loss: 0.9391
Epoch 1/10... Discriminator Loss: 0.9588... Generator Loss: 0.8832
Epoch 1/10... Discriminator Loss: 1.3492... Generator Loss: 0.4466
Epoch 1/10... Discriminator Loss: 1.6223... Generator Loss: 2.4649
Epoch 1/10... Discriminator Loss: 0.8152... Generator Loss: 1.2836
Epoch 1/10... Discriminator Loss: 1.2507... Generator Loss: 0.7667
Epoch 1/10... Discriminator Loss: 0.8526... Generator Loss: 2.6526
Epoch 1/10... Discriminator Loss: 0.9007... Generator Loss: 0.7937
Epoch 1/10... Discriminator Loss: 3.1377... Generator Loss: 4.3016
Epoch 1/10... Discriminator Loss: 1.2659... Generator Loss: 0.5287
Epoch 1/10... Discriminator Loss: 0.9863... Generator Loss: 0.7985
Epoch 1/10... Discriminator Loss: 1.0781... Generator Loss: 2.4381
Epoch 1/10... Discriminator Loss: 2.4255... Generator Loss: 0.1485
Epoch 1/10... Discriminator Loss: 1.2064... Generator Loss: 0.6782
Epoch 1/10... Discriminator Loss: 1.5615... Generator Loss: 0.4359
Epoch 1/10... Discriminator Loss: 1.4066... Generator Loss: 1.7091
Epoch 1/10... Discriminator Loss: 1.1321... Generator Loss: 1.1165
Epoch 1/10... Discriminator Loss: 1.2018... Generator Loss: 0.5643
Epoch 1/10... Discriminator Loss: 1.2323... Generator Loss: 0.8638
Epoch 1/10... Discriminator Loss: 0.8285... Generator Loss: 1.1594
Epoch 1/10... Discriminator Loss: 0.8553... Generator Loss: 1.0081
Epoch 1/10... Discriminator Loss: 0.9789... Generator Loss: 0.9227
Epoch 1/10... Discriminator Loss: 0.9257... Generator Loss: 1.9976
Epoch 1/10... Discriminator Loss: 0.8331... Generator Loss: 1.1248
Epoch 1/10... Discriminator Loss: 1.3665... Generator Loss: 1.0104
Epoch 1/10... Discriminator Loss: 1.5025... Generator Loss: 2.4210
Epoch 1/10... Discriminator Loss: 0.8209... Generator Loss: 1.0000
Epoch 1/10... Discriminator Loss: 1.5107... Generator Loss: 0.5622
Epoch 1/10... Discriminator Loss: 1.1728... Generator Loss: 1.1316
Epoch 1/10... Discriminator Loss: 1.2491... Generator Loss: 0.5996
Epoch 1/10... Discriminator Loss: 0.5825... Generator Loss: 1.7237
Epoch 1/10... Discriminator Loss: 0.6379... Generator Loss: 1.9039
Epoch 1/10... Discriminator Loss: 1.9717... Generator Loss: 1.9419
Epoch 1/10... Discriminator Loss: 0.9330... Generator Loss: 0.9588
Epoch 1/10... Discriminator Loss: 1.1905... Generator Loss: 0.7697
Epoch 1/10... Discriminator Loss: 0.8277... Generator Loss: 0.9243
Epoch 1/10... Discriminator Loss: 1.4604... Generator Loss: 0.9177
Epoch 1/10... Discriminator Loss: 1.0069... Generator Loss: 0.6862
Epoch 1/10... Discriminator Loss: 1.5435... Generator Loss: 2.4149
Epoch 1/10... Discriminator Loss: 1.1034... Generator Loss: 0.7502
Epoch 1/10... Discriminator Loss: 1.8213... Generator Loss: 2.3121
Epoch 1/10... Discriminator Loss: 1.1308... Generator Loss: 0.6620
Epoch 1/10... Discriminator Loss: 1.4200... Generator Loss: 1.2107
Epoch 1/10... Discriminator Loss: 1.5498... Generator Loss: 0.3297
Epoch 1/10... Discriminator Loss: 2.0869... Generator Loss: 0.1728
Epoch 1/10... Discriminator Loss: 0.6997... Generator Loss: 3.2646
Epoch 1/10... Discriminator Loss: 1.2468... Generator Loss: 0.4783
Epoch 1/10... Discriminator Loss: 1.4540... Generator Loss: 0.7028
Epoch 1/10... Discriminator Loss: 0.9434... Generator Loss: 0.7458
Epoch 1/10... Discriminator Loss: 1.4313... Generator Loss: 0.7894
Epoch 1/10... Discriminator Loss: 1.3440... Generator Loss: 0.8418
Epoch 1/10... Discriminator Loss: 1.5325... Generator Loss: 0.7001
Epoch 1/10... Discriminator Loss: 1.4643... Generator Loss: 0.6816
Epoch 1/10... Discriminator Loss: 1.2924... Generator Loss: 0.4509
Epoch 1/10... Discriminator Loss: 1.0350... Generator Loss: 1.1683
Epoch 1/10... Discriminator Loss: 0.9850... Generator Loss: 0.6433
Epoch 1/10... Discriminator Loss: 1.1701... Generator Loss: 0.8392
Epoch 1/10... Discriminator Loss: 1.3907... Generator Loss: 1.0299
Epoch 1/10... Discriminator Loss: 1.2025... Generator Loss: 1.7276
Epoch 1/10... Discriminator Loss: 1.5110... Generator Loss: 0.3539
Epoch 1/10... Discriminator Loss: 1.2259... Generator Loss: 0.6046
Epoch 1/10... Discriminator Loss: 1.0039... Generator Loss: 1.1449
Epoch 1/10... Discriminator Loss: 0.6880... Generator Loss: 2.0833
Epoch 1/10... Discriminator Loss: 0.9745... Generator Loss: 1.0808
Epoch 1/10... Discriminator Loss: 1.3936... Generator Loss: 0.5103
Epoch 1/10... Discriminator Loss: 1.2053... Generator Loss: 1.0197
Epoch 1/10... Discriminator Loss: 1.3813... Generator Loss: 0.7834
Epoch 1/10... Discriminator Loss: 1.0243... Generator Loss: 0.7542
Epoch 1/10... Discriminator Loss: 1.2378... Generator Loss: 0.6590
Epoch 1/10... Discriminator Loss: 0.8854... Generator Loss: 1.2051
Epoch 1/10... Discriminator Loss: 1.4012... Generator Loss: 0.4380
Epoch 1/10... Discriminator Loss: 1.0381... Generator Loss: 1.5970
Epoch 1/10... Discriminator Loss: 2.3109... Generator Loss: 2.9609
Epoch 1/10... Discriminator Loss: 1.0284... Generator Loss: 1.4217
Epoch 1/10... Discriminator Loss: 1.7867... Generator Loss: 0.2218
Epoch 1/10... Discriminator Loss: 1.2541... Generator Loss: 0.7543
Epoch 1/10... Discriminator Loss: 1.1909... Generator Loss: 1.0019
Epoch 1/10... Discriminator Loss: 0.9969... Generator Loss: 0.6630
Epoch 1/10... Discriminator Loss: 1.0629... Generator Loss: 0.6177
Epoch 1/10... Discriminator Loss: 0.8892... Generator Loss: 0.7290
Epoch 1/10... Discriminator Loss: 1.1223... Generator Loss: 0.5988
Epoch 1/10... Discriminator Loss: 1.0968... Generator Loss: 1.1249
Epoch 1/10... Discriminator Loss: 1.1798... Generator Loss: 0.6849
Epoch 1/10... Discriminator Loss: 0.8256... Generator Loss: 1.1838
Epoch 1/10... Discriminator Loss: 1.1104... Generator Loss: 2.0245
Epoch 1/10... Discriminator Loss: 1.2597... Generator Loss: 1.5421
Epoch 1/10... Discriminator Loss: 2.6950... Generator Loss: 3.3389
Epoch 1/10... Discriminator Loss: 0.8289... Generator Loss: 1.6911
Epoch 1/10... Discriminator Loss: 1.0863... Generator Loss: 0.5980
Epoch 1/10... Discriminator Loss: 1.2148... Generator Loss: 1.0124
Epoch 1/10... Discriminator Loss: 1.2540... Generator Loss: 0.4822
Epoch 1/10... Discriminator Loss: 0.7673... Generator Loss: 1.0932
Epoch 1/10... Discriminator Loss: 1.1019... Generator Loss: 0.7485
Epoch 1/10... Discriminator Loss: 0.4491... Generator Loss: 1.6708
Epoch 1/10... Discriminator Loss: 1.1173... Generator Loss: 0.8152
Epoch 1/10... Discriminator Loss: 1.9892... Generator Loss: 0.1871
Epoch 1/10... Discriminator Loss: 1.2552... Generator Loss: 1.1802
Epoch 1/10... Discriminator Loss: 0.9235... Generator Loss: 1.2353
Epoch 1/10... Discriminator Loss: 0.9390... Generator Loss: 0.9757
Epoch 1/10... Discriminator Loss: 0.8931... Generator Loss: 1.0683
Epoch 1/10... Discriminator Loss: 1.4253... Generator Loss: 0.4292
Epoch 1/10... Discriminator Loss: 1.4326... Generator Loss: 2.1674
Epoch 1/10... Discriminator Loss: 1.1420... Generator Loss: 0.5261
Epoch 1/10... Discriminator Loss: 1.2288... Generator Loss: 0.7783
Epoch 1/10... Discriminator Loss: 1.0737... Generator Loss: 0.7297
Epoch 1/10... Discriminator Loss: 1.2048... Generator Loss: 0.6676
Epoch 1/10... Discriminator Loss: 1.2107... Generator Loss: 1.3369
Epoch 1/10... Discriminator Loss: 0.9939... Generator Loss: 0.7805
Epoch 1/10... Discriminator Loss: 1.0592... Generator Loss: 0.6039
Epoch 1/10... Discriminator Loss: 0.5081... Generator Loss: 1.5161
Epoch 1/10... Discriminator Loss: 1.7899... Generator Loss: 2.7148
Epoch 1/10... Discriminator Loss: 1.1280... Generator Loss: 0.5503
Epoch 1/10... Discriminator Loss: 1.2522... Generator Loss: 0.4848
Epoch 1/10... Discriminator Loss: 1.3042... Generator Loss: 0.5184
Epoch 1/10... Discriminator Loss: 1.0477... Generator Loss: 1.1536
Epoch 1/10... Discriminator Loss: 0.7705... Generator Loss: 1.7805
Epoch 1/10... Discriminator Loss: 1.0261... Generator Loss: 0.5767
Epoch 1/10... Discriminator Loss: 2.0399... Generator Loss: 0.1706
Epoch 1/10... Discriminator Loss: 1.3742... Generator Loss: 0.8246
Epoch 1/10... Discriminator Loss: 1.4344... Generator Loss: 1.1755
Epoch 1/10... Discriminator Loss: 0.9529... Generator Loss: 0.7463
Epoch 1/10... Discriminator Loss: 1.3758... Generator Loss: 0.6276
Epoch 1/10... Discriminator Loss: 1.2998... Generator Loss: 0.5124
Epoch 1/10... Discriminator Loss: 1.3254... Generator Loss: 1.1480
Epoch 1/10... Discriminator Loss: 1.2118... Generator Loss: 0.8173
Epoch 1/10... Discriminator Loss: 1.0539... Generator Loss: 1.3567
Epoch 1/10... Discriminator Loss: 1.0522... Generator Loss: 0.8604
Epoch 1/10... Discriminator Loss: 1.3185... Generator Loss: 1.0446
Epoch 1/10... Discriminator Loss: 1.2457... Generator Loss: 0.4959
Epoch 1/10... Discriminator Loss: 0.9034... Generator Loss: 2.0805
Epoch 1/10... Discriminator Loss: 1.1824... Generator Loss: 0.4878
Epoch 1/10... Discriminator Loss: 1.0384... Generator Loss: 0.9514
Epoch 1/10... Discriminator Loss: 1.0915... Generator Loss: 0.5918
Epoch 1/10... Discriminator Loss: 0.9267... Generator Loss: 1.2954
Epoch 1/10... Discriminator Loss: 0.6524... Generator Loss: 2.0625
Epoch 1/10... Discriminator Loss: 1.2148... Generator Loss: 1.8331
Epoch 1/10... Discriminator Loss: 1.6511... Generator Loss: 0.2778
Epoch 1/10... Discriminator Loss: 1.5406... Generator Loss: 0.3410
Epoch 1/10... Discriminator Loss: 1.3957... Generator Loss: 0.4634
Epoch 1/10... Discriminator Loss: 0.7424... Generator Loss: 1.2151
Epoch 2/10... Discriminator Loss: 1.4291... Generator Loss: 1.7062
Epoch 2/10... Discriminator Loss: 0.7272... Generator Loss: 0.8724
Epoch 2/10... Discriminator Loss: 1.4778... Generator Loss: 0.4193
Epoch 2/10... Discriminator Loss: 2.3098... Generator Loss: 2.4184
Epoch 2/10... Discriminator Loss: 0.4430... Generator Loss: 1.6985
Epoch 2/10... Discriminator Loss: 1.2234... Generator Loss: 0.5237
Epoch 2/10... Discriminator Loss: 1.3176... Generator Loss: 0.5966
Epoch 2/10... Discriminator Loss: 1.3816... Generator Loss: 1.0534
Epoch 2/10... Discriminator Loss: 1.1154... Generator Loss: 0.5566
Epoch 2/10... Discriminator Loss: 1.5439... Generator Loss: 1.4876
Epoch 2/10... Discriminator Loss: 1.0459... Generator Loss: 0.6601
Epoch 2/10... Discriminator Loss: 0.8026... Generator Loss: 0.9957
Epoch 2/10... Discriminator Loss: 1.4621... Generator Loss: 0.5272
Epoch 2/10... Discriminator Loss: 1.0772... Generator Loss: 0.8925
Epoch 2/10... Discriminator Loss: 1.7711... Generator Loss: 0.2421
Epoch 2/10... Discriminator Loss: 1.0890... Generator Loss: 0.9829
Epoch 2/10... Discriminator Loss: 0.8487... Generator Loss: 1.5912
Epoch 2/10... Discriminator Loss: 1.2171... Generator Loss: 0.7821
Epoch 2/10... Discriminator Loss: 1.1479... Generator Loss: 0.5916
Epoch 2/10... Discriminator Loss: 1.4444... Generator Loss: 0.4191
Epoch 2/10... Discriminator Loss: 1.1512... Generator Loss: 1.1382
Epoch 2/10... Discriminator Loss: 1.2496... Generator Loss: 0.6756
Epoch 2/10... Discriminator Loss: 1.0842... Generator Loss: 0.9560
Epoch 2/10... Discriminator Loss: 1.2425... Generator Loss: 0.9468
Epoch 2/10... Discriminator Loss: 1.4684... Generator Loss: 1.6679
Epoch 2/10... Discriminator Loss: 1.0540... Generator Loss: 0.6548
Epoch 2/10... Discriminator Loss: 1.2104... Generator Loss: 0.7926
Epoch 2/10... Discriminator Loss: 1.5605... Generator Loss: 1.9718
Epoch 2/10... Discriminator Loss: 1.1555... Generator Loss: 0.5932
Epoch 2/10... Discriminator Loss: 1.3083... Generator Loss: 0.8683
Epoch 2/10... Discriminator Loss: 1.0674... Generator Loss: 1.0637
Epoch 2/10... Discriminator Loss: 1.0610... Generator Loss: 0.7878
Epoch 2/10... Discriminator Loss: 1.6983... Generator Loss: 0.2503
Epoch 2/10... Discriminator Loss: 1.1153... Generator Loss: 0.9078
Epoch 2/10... Discriminator Loss: 1.2208... Generator Loss: 1.3844
Epoch 2/10... Discriminator Loss: 1.4926... Generator Loss: 0.3315
Epoch 2/10... Discriminator Loss: 0.7046... Generator Loss: 1.3034
Epoch 2/10... Discriminator Loss: 1.3163... Generator Loss: 0.4275
Epoch 2/10... Discriminator Loss: 0.9605... Generator Loss: 0.7932
Epoch 2/10... Discriminator Loss: 1.0398... Generator Loss: 1.1090
Epoch 2/10... Discriminator Loss: 1.2580... Generator Loss: 0.4968
Epoch 2/10... Discriminator Loss: 1.1263... Generator Loss: 0.6058
Epoch 2/10... Discriminator Loss: 1.5741... Generator Loss: 0.3936
Epoch 2/10... Discriminator Loss: 1.0828... Generator Loss: 0.7179
Epoch 2/10... Discriminator Loss: 0.4262... Generator Loss: 2.4526
Epoch 2/10... Discriminator Loss: 1.7753... Generator Loss: 0.2863
Epoch 2/10... Discriminator Loss: 1.1173... Generator Loss: 0.9362
Epoch 2/10... Discriminator Loss: 1.2299... Generator Loss: 0.6031
Epoch 2/10... Discriminator Loss: 1.1068... Generator Loss: 0.5981
Epoch 2/10... Discriminator Loss: 0.9087... Generator Loss: 0.7384
Epoch 2/10... Discriminator Loss: 0.9632... Generator Loss: 0.7363
Epoch 2/10... Discriminator Loss: 1.2896... Generator Loss: 0.4885
Epoch 2/10... Discriminator Loss: 1.5313... Generator Loss: 0.3536
Epoch 2/10... Discriminator Loss: 1.2492... Generator Loss: 0.5883
Epoch 2/10... Discriminator Loss: 1.1530... Generator Loss: 0.6273
Epoch 2/10... Discriminator Loss: 1.1166... Generator Loss: 1.3096
Epoch 2/10... Discriminator Loss: 0.9369... Generator Loss: 0.7111
Epoch 2/10... Discriminator Loss: 0.9410... Generator Loss: 0.8303
Epoch 2/10... Discriminator Loss: 1.5039... Generator Loss: 0.9801
Epoch 2/10... Discriminator Loss: 1.4134... Generator Loss: 0.5215
Epoch 2/10... Discriminator Loss: 1.3003... Generator Loss: 0.6890
Epoch 2/10... Discriminator Loss: 1.1835... Generator Loss: 1.2089
Epoch 2/10... Discriminator Loss: 1.1726... Generator Loss: 0.6169
Epoch 2/10... Discriminator Loss: 0.9284... Generator Loss: 0.9115
Epoch 2/10... Discriminator Loss: 1.3198... Generator Loss: 0.4797
Epoch 2/10... Discriminator Loss: 1.2578... Generator Loss: 0.5352
Epoch 2/10... Discriminator Loss: 1.2299... Generator Loss: 0.5924
Epoch 2/10... Discriminator Loss: 0.8700... Generator Loss: 0.9719
Epoch 2/10... Discriminator Loss: 1.1323... Generator Loss: 0.5580
Epoch 2/10... Discriminator Loss: 1.1556... Generator Loss: 0.6147
Epoch 2/10... Discriminator Loss: 1.3121... Generator Loss: 0.6754
Epoch 2/10... Discriminator Loss: 1.4198... Generator Loss: 1.6246
Epoch 2/10... Discriminator Loss: 1.4526... Generator Loss: 0.3899
Epoch 2/10... Discriminator Loss: 1.0506... Generator Loss: 0.7731
Epoch 2/10... Discriminator Loss: 1.4503... Generator Loss: 1.1944
Epoch 2/10... Discriminator Loss: 1.4367... Generator Loss: 0.5476
Epoch 2/10... Discriminator Loss: 0.8647... Generator Loss: 0.9314
Epoch 2/10... Discriminator Loss: 1.1685... Generator Loss: 0.8183
Epoch 2/10... Discriminator Loss: 1.4635... Generator Loss: 0.8083
Epoch 2/10... Discriminator Loss: 1.4101... Generator Loss: 0.3767
Epoch 2/10... Discriminator Loss: 0.8382... Generator Loss: 1.0158
Epoch 2/10... Discriminator Loss: 1.1080... Generator Loss: 1.0382
Epoch 2/10... Discriminator Loss: 1.3034... Generator Loss: 0.5206
Epoch 2/10... Discriminator Loss: 1.3458... Generator Loss: 0.6579
Epoch 2/10... Discriminator Loss: 1.2960... Generator Loss: 0.4917
Epoch 2/10... Discriminator Loss: 1.5016... Generator Loss: 2.5180
Epoch 2/10... Discriminator Loss: 1.5121... Generator Loss: 0.7200
Epoch 2/10... Discriminator Loss: 0.8677... Generator Loss: 0.8216
Epoch 2/10... Discriminator Loss: 1.3080... Generator Loss: 1.1460
Epoch 2/10... Discriminator Loss: 0.7900... Generator Loss: 1.4406
Epoch 2/10... Discriminator Loss: 1.4005... Generator Loss: 0.3878
Epoch 2/10... Discriminator Loss: 1.1071... Generator Loss: 0.7770
Epoch 2/10... Discriminator Loss: 0.9837... Generator Loss: 0.9625
Epoch 2/10... Discriminator Loss: 1.4946... Generator Loss: 0.3225
Epoch 2/10... Discriminator Loss: 1.2311... Generator Loss: 0.4507
Epoch 2/10... Discriminator Loss: 1.0226... Generator Loss: 1.2107
Epoch 2/10... Discriminator Loss: 1.4074... Generator Loss: 0.5218
Epoch 2/10... Discriminator Loss: 1.0601... Generator Loss: 0.6459
Epoch 2/10... Discriminator Loss: 1.3043... Generator Loss: 1.1390
Epoch 2/10... Discriminator Loss: 0.5584... Generator Loss: 1.1935
Epoch 2/10... Discriminator Loss: 1.6260... Generator Loss: 1.6471
Epoch 2/10... Discriminator Loss: 1.3554... Generator Loss: 0.6532
Epoch 2/10... Discriminator Loss: 1.1925... Generator Loss: 0.8139
Epoch 2/10... Discriminator Loss: 1.2224... Generator Loss: 0.9233
Epoch 2/10... Discriminator Loss: 1.0561... Generator Loss: 0.8630
Epoch 2/10... Discriminator Loss: 1.3742... Generator Loss: 0.6459
Epoch 2/10... Discriminator Loss: 1.5515... Generator Loss: 0.4143
Epoch 2/10... Discriminator Loss: 1.7493... Generator Loss: 0.2545
Epoch 2/10... Discriminator Loss: 1.2087... Generator Loss: 1.1333
Epoch 2/10... Discriminator Loss: 1.2564... Generator Loss: 0.6146
Epoch 2/10... Discriminator Loss: 1.8769... Generator Loss: 2.1637
Epoch 2/10... Discriminator Loss: 0.9684... Generator Loss: 1.1371
Epoch 2/10... Discriminator Loss: 1.2748... Generator Loss: 0.4863
Epoch 2/10... Discriminator Loss: 1.5463... Generator Loss: 0.5089
Epoch 2/10... Discriminator Loss: 1.2705... Generator Loss: 0.6300
Epoch 2/10... Discriminator Loss: 1.0945... Generator Loss: 1.6804
Epoch 2/10... Discriminator Loss: 1.1696... Generator Loss: 0.6935
Epoch 2/10... Discriminator Loss: 1.1460... Generator Loss: 0.9985
Epoch 2/10... Discriminator Loss: 0.6120... Generator Loss: 1.9151
Epoch 2/10... Discriminator Loss: 1.2587... Generator Loss: 0.8651
Epoch 2/10... Discriminator Loss: 1.0071... Generator Loss: 1.0658
Epoch 2/10... Discriminator Loss: 1.3000... Generator Loss: 0.4662
Epoch 2/10... Discriminator Loss: 1.7507... Generator Loss: 2.0070
Epoch 2/10... Discriminator Loss: 1.2811... Generator Loss: 0.9604
Epoch 2/10... Discriminator Loss: 0.8842... Generator Loss: 1.0381
Epoch 2/10... Discriminator Loss: 1.4633... Generator Loss: 0.3901
Epoch 2/10... Discriminator Loss: 1.1973... Generator Loss: 0.6931
Epoch 2/10... Discriminator Loss: 1.4894... Generator Loss: 1.2457
Epoch 2/10... Discriminator Loss: 1.2530... Generator Loss: 0.5382
Epoch 2/10... Discriminator Loss: 1.1488... Generator Loss: 0.5610
Epoch 2/10... Discriminator Loss: 1.0328... Generator Loss: 0.8220
Epoch 2/10... Discriminator Loss: 1.3670... Generator Loss: 0.5139
Epoch 2/10... Discriminator Loss: 1.6041... Generator Loss: 0.3485
Epoch 2/10... Discriminator Loss: 1.2177... Generator Loss: 0.5297
Epoch 2/10... Discriminator Loss: 1.3210... Generator Loss: 0.5884
Epoch 2/10... Discriminator Loss: 1.1732... Generator Loss: 0.6396
Epoch 2/10... Discriminator Loss: 1.2840... Generator Loss: 0.5631
Epoch 2/10... Discriminator Loss: 1.2095... Generator Loss: 1.2131
Epoch 2/10... Discriminator Loss: 1.2610... Generator Loss: 0.6917
Epoch 2/10... Discriminator Loss: 1.2118... Generator Loss: 0.6365
Epoch 2/10... Discriminator Loss: 0.6392... Generator Loss: 1.3387
Epoch 2/10... Discriminator Loss: 1.4065... Generator Loss: 0.5473
Epoch 2/10... Discriminator Loss: 1.1404... Generator Loss: 0.6815
Epoch 2/10... Discriminator Loss: 1.2541... Generator Loss: 0.5111
Epoch 2/10... Discriminator Loss: 1.2018... Generator Loss: 0.6212
Epoch 2/10... Discriminator Loss: 1.3473... Generator Loss: 0.4317
Epoch 2/10... Discriminator Loss: 1.4982... Generator Loss: 0.3267
Epoch 2/10... Discriminator Loss: 1.1887... Generator Loss: 0.7901
Epoch 2/10... Discriminator Loss: 1.6922... Generator Loss: 1.8545
Epoch 2/10... Discriminator Loss: 1.2271... Generator Loss: 0.9444
Epoch 2/10... Discriminator Loss: 1.5591... Generator Loss: 1.5089
Epoch 2/10... Discriminator Loss: 1.1545... Generator Loss: 0.9121
Epoch 2/10... Discriminator Loss: 1.0707... Generator Loss: 0.6886
Epoch 2/10... Discriminator Loss: 1.0257... Generator Loss: 1.1344
Epoch 2/10... Discriminator Loss: 1.4728... Generator Loss: 0.4219
Epoch 2/10... Discriminator Loss: 1.0010... Generator Loss: 0.9237
Epoch 2/10... Discriminator Loss: 1.3848... Generator Loss: 0.4067
Epoch 2/10... Discriminator Loss: 1.2194... Generator Loss: 1.0127
Epoch 3/10... Discriminator Loss: 1.2320... Generator Loss: 0.5615
Epoch 3/10... Discriminator Loss: 1.2842... Generator Loss: 0.7404
Epoch 3/10... Discriminator Loss: 1.3194... Generator Loss: 0.8557
Epoch 3/10... Discriminator Loss: 1.6818... Generator Loss: 1.9252
Epoch 3/10... Discriminator Loss: 1.2586... Generator Loss: 0.9303
Epoch 3/10... Discriminator Loss: 1.4158... Generator Loss: 0.4059
Epoch 3/10... Discriminator Loss: 1.3003... Generator Loss: 0.5720
Epoch 3/10... Discriminator Loss: 1.2951... Generator Loss: 0.6004
Epoch 3/10... Discriminator Loss: 1.3577... Generator Loss: 0.6822
Epoch 3/10... Discriminator Loss: 1.0866... Generator Loss: 0.8749
Epoch 3/10... Discriminator Loss: 1.3527... Generator Loss: 0.4674
Epoch 3/10... Discriminator Loss: 1.5907... Generator Loss: 0.3323
Epoch 3/10... Discriminator Loss: 1.3389... Generator Loss: 1.0319
Epoch 3/10... Discriminator Loss: 1.2859... Generator Loss: 0.7825
Epoch 3/10... Discriminator Loss: 1.3092... Generator Loss: 0.6693
Epoch 3/10... Discriminator Loss: 1.0167... Generator Loss: 1.1120
Epoch 3/10... Discriminator Loss: 1.2413... Generator Loss: 0.9221
Epoch 3/10... Discriminator Loss: 1.3323... Generator Loss: 0.7170
Epoch 3/10... Discriminator Loss: 1.5022... Generator Loss: 0.3820
Epoch 3/10... Discriminator Loss: 1.2678... Generator Loss: 1.0077
Epoch 3/10... Discriminator Loss: 0.9904... Generator Loss: 0.7880
Epoch 3/10... Discriminator Loss: 1.2889... Generator Loss: 0.5395
Epoch 3/10... Discriminator Loss: 1.3844... Generator Loss: 0.4392
Epoch 3/10... Discriminator Loss: 1.4135... Generator Loss: 1.1280
Epoch 3/10... Discriminator Loss: 1.1182... Generator Loss: 1.2065
Epoch 3/10... Discriminator Loss: 1.2579... Generator Loss: 0.6157
Epoch 3/10... Discriminator Loss: 0.9622... Generator Loss: 0.9178
Epoch 3/10... Discriminator Loss: 1.1937... Generator Loss: 0.6724
Epoch 3/10... Discriminator Loss: 1.3392... Generator Loss: 0.8129
Epoch 3/10... Discriminator Loss: 0.9051... Generator Loss: 1.1331
Epoch 3/10... Discriminator Loss: 1.2893... Generator Loss: 0.5763
Epoch 3/10... Discriminator Loss: 1.3727... Generator Loss: 1.5521
Epoch 3/10... Discriminator Loss: 1.3923... Generator Loss: 0.6811
Epoch 3/10... Discriminator Loss: 1.3507... Generator Loss: 0.7699
Epoch 3/10... Discriminator Loss: 1.0799... Generator Loss: 0.8473
Epoch 3/10... Discriminator Loss: 1.2212... Generator Loss: 0.5871
Epoch 3/10... Discriminator Loss: 1.2787... Generator Loss: 0.9205
Epoch 3/10... Discriminator Loss: 1.1500... Generator Loss: 0.6703
Epoch 3/10... Discriminator Loss: 1.0710... Generator Loss: 0.9395
Epoch 3/10... Discriminator Loss: 1.6898... Generator Loss: 0.2751
Epoch 3/10... Discriminator Loss: 1.4595... Generator Loss: 0.3724
Epoch 3/10... Discriminator Loss: 1.4223... Generator Loss: 0.4466
Epoch 3/10... Discriminator Loss: 1.4349... Generator Loss: 0.8349
Epoch 3/10... Discriminator Loss: 1.1332... Generator Loss: 0.6280
Epoch 3/10... Discriminator Loss: 1.6295... Generator Loss: 0.2925
Epoch 3/10... Discriminator Loss: 1.1843... Generator Loss: 0.6914
Epoch 3/10... Discriminator Loss: 1.3583... Generator Loss: 1.0906
Epoch 3/10... Discriminator Loss: 1.2651... Generator Loss: 0.7644
Epoch 3/10... Discriminator Loss: 1.2102... Generator Loss: 1.3332
Epoch 3/10... Discriminator Loss: 1.2964... Generator Loss: 0.5177
Epoch 3/10... Discriminator Loss: 0.9082... Generator Loss: 0.9810
Epoch 3/10... Discriminator Loss: 1.3519... Generator Loss: 0.7314
Epoch 3/10... Discriminator Loss: 1.2145... Generator Loss: 0.5492
Epoch 3/10... Discriminator Loss: 1.1740... Generator Loss: 0.9507
Epoch 3/10... Discriminator Loss: 1.3672... Generator Loss: 0.4920
Epoch 3/10... Discriminator Loss: 1.0474... Generator Loss: 1.0515
Epoch 3/10... Discriminator Loss: 1.3526... Generator Loss: 0.4668
Epoch 3/10... Discriminator Loss: 1.3035... Generator Loss: 0.4520
Epoch 3/10... Discriminator Loss: 1.2173... Generator Loss: 0.4928
Epoch 3/10... Discriminator Loss: 1.7660... Generator Loss: 1.6970
Epoch 3/10... Discriminator Loss: 1.2810... Generator Loss: 1.1318
Epoch 3/10... Discriminator Loss: 1.2416... Generator Loss: 0.7340
Epoch 3/10... Discriminator Loss: 1.5752... Generator Loss: 1.3019
Epoch 3/10... Discriminator Loss: 1.1652... Generator Loss: 0.6004
Epoch 3/10... Discriminator Loss: 1.2600... Generator Loss: 0.8677
Epoch 3/10... Discriminator Loss: 1.4951... Generator Loss: 0.3838
Epoch 3/10... Discriminator Loss: 0.9282... Generator Loss: 1.0727
Epoch 3/10... Discriminator Loss: 1.2511... Generator Loss: 0.9342
Epoch 3/10... Discriminator Loss: 1.8445... Generator Loss: 1.7008
Epoch 3/10... Discriminator Loss: 1.2023... Generator Loss: 0.9681
Epoch 3/10... Discriminator Loss: 1.3102... Generator Loss: 0.5525
Epoch 3/10... Discriminator Loss: 0.5087... Generator Loss: 2.0060
Epoch 3/10... Discriminator Loss: 1.6837... Generator Loss: 0.2733
Epoch 3/10... Discriminator Loss: 1.4507... Generator Loss: 0.6080
Epoch 3/10... Discriminator Loss: 1.1565... Generator Loss: 0.7699
Epoch 3/10... Discriminator Loss: 1.3785... Generator Loss: 0.9191
Epoch 3/10... Discriminator Loss: 1.2962... Generator Loss: 0.5832
Epoch 3/10... Discriminator Loss: 1.2759... Generator Loss: 0.8592
Epoch 3/10... Discriminator Loss: 1.3672... Generator Loss: 0.4233
Epoch 3/10... Discriminator Loss: 1.2730... Generator Loss: 0.6013
Epoch 3/10... Discriminator Loss: 1.1830... Generator Loss: 0.6807
Epoch 3/10... Discriminator Loss: 2.0118... Generator Loss: 1.8256
Epoch 3/10... Discriminator Loss: 1.0826... Generator Loss: 0.8026
Epoch 3/10... Discriminator Loss: 1.2882... Generator Loss: 0.5730
Epoch 3/10... Discriminator Loss: 1.1591... Generator Loss: 0.7738
Epoch 3/10... Discriminator Loss: 1.3683... Generator Loss: 0.5883
Epoch 3/10... Discriminator Loss: 1.3566... Generator Loss: 0.6652
Epoch 3/10... Discriminator Loss: 1.4280... Generator Loss: 0.9367
Epoch 3/10... Discriminator Loss: 1.3700... Generator Loss: 1.5558
Epoch 3/10... Discriminator Loss: 1.3019... Generator Loss: 1.0456
Epoch 3/10... Discriminator Loss: 1.3273... Generator Loss: 0.6248
Epoch 3/10... Discriminator Loss: 1.1369... Generator Loss: 1.2131
Epoch 3/10... Discriminator Loss: 1.4733... Generator Loss: 0.8588
Epoch 3/10... Discriminator Loss: 1.4408... Generator Loss: 0.4112
Epoch 3/10... Discriminator Loss: 1.0600... Generator Loss: 0.7400
Epoch 3/10... Discriminator Loss: 1.8175... Generator Loss: 0.2314
Epoch 3/10... Discriminator Loss: 1.2999... Generator Loss: 0.5852
Epoch 3/10... Discriminator Loss: 1.5208... Generator Loss: 1.3629
Epoch 3/10... Discriminator Loss: 1.2926... Generator Loss: 0.7109
Epoch 3/10... Discriminator Loss: 1.4523... Generator Loss: 0.8073
Epoch 3/10... Discriminator Loss: 1.2035... Generator Loss: 1.1305
Epoch 3/10... Discriminator Loss: 1.3921... Generator Loss: 0.4647
Epoch 3/10... Discriminator Loss: 1.7386... Generator Loss: 0.2744
Epoch 3/10... Discriminator Loss: 1.1884... Generator Loss: 0.6125
Epoch 3/10... Discriminator Loss: 1.3565... Generator Loss: 0.4809
Epoch 3/10... Discriminator Loss: 1.3507... Generator Loss: 0.6214
Epoch 3/10... Discriminator Loss: 1.2410... Generator Loss: 0.6134
Epoch 3/10... Discriminator Loss: 1.3781... Generator Loss: 0.7462
Epoch 3/10... Discriminator Loss: 1.3350... Generator Loss: 0.5267
Epoch 3/10... Discriminator Loss: 1.2934... Generator Loss: 0.8678
Epoch 3/10... Discriminator Loss: 1.3002... Generator Loss: 0.7842
Epoch 3/10... Discriminator Loss: 1.3941... Generator Loss: 0.5123
Epoch 3/10... Discriminator Loss: 1.4673... Generator Loss: 0.8220
Epoch 3/10... Discriminator Loss: 1.3800... Generator Loss: 0.5129
Epoch 3/10... Discriminator Loss: 1.2507... Generator Loss: 0.6230
Epoch 3/10... Discriminator Loss: 1.2390... Generator Loss: 0.6517
Epoch 3/10... Discriminator Loss: 1.3229... Generator Loss: 0.4876
Epoch 3/10... Discriminator Loss: 1.5255... Generator Loss: 0.4049
Epoch 3/10... Discriminator Loss: 1.4130... Generator Loss: 0.4421
Epoch 3/10... Discriminator Loss: 1.5662... Generator Loss: 0.4099
Epoch 3/10... Discriminator Loss: 1.3908... Generator Loss: 1.5028
Epoch 3/10... Discriminator Loss: 1.1333... Generator Loss: 1.1218
Epoch 3/10... Discriminator Loss: 1.2235... Generator Loss: 0.8054
Epoch 3/10... Discriminator Loss: 1.1099... Generator Loss: 0.9658
Epoch 3/10... Discriminator Loss: 1.1837... Generator Loss: 0.7411
Epoch 3/10... Discriminator Loss: 1.2441... Generator Loss: 0.5794
Epoch 3/10... Discriminator Loss: 1.3884... Generator Loss: 0.5424
Epoch 3/10... Discriminator Loss: 1.6202... Generator Loss: 0.9081
Epoch 3/10... Discriminator Loss: 1.4766... Generator Loss: 0.3929
Epoch 3/10... Discriminator Loss: 1.0925... Generator Loss: 0.7442
Epoch 3/10... Discriminator Loss: 1.5724... Generator Loss: 0.3416
Epoch 3/10... Discriminator Loss: 1.4417... Generator Loss: 0.7716
Epoch 3/10... Discriminator Loss: 1.5264... Generator Loss: 0.4694
Epoch 3/10... Discriminator Loss: 1.4896... Generator Loss: 0.4826
Epoch 3/10... Discriminator Loss: 1.4130... Generator Loss: 0.5942
Epoch 3/10... Discriminator Loss: 1.2661... Generator Loss: 0.6017
Epoch 3/10... Discriminator Loss: 1.0287... Generator Loss: 1.2466
Epoch 3/10... Discriminator Loss: 1.0602... Generator Loss: 0.7460
Epoch 3/10... Discriminator Loss: 1.3141... Generator Loss: 0.7346
Epoch 3/10... Discriminator Loss: 1.3695... Generator Loss: 0.4439
Epoch 3/10... Discriminator Loss: 1.2129... Generator Loss: 0.7135
Epoch 3/10... Discriminator Loss: 1.3894... Generator Loss: 0.4371
Epoch 3/10... Discriminator Loss: 1.1237... Generator Loss: 0.7285
Epoch 3/10... Discriminator Loss: 1.7473... Generator Loss: 0.2599
Epoch 3/10... Discriminator Loss: 1.2630... Generator Loss: 0.7118
Epoch 3/10... Discriminator Loss: 1.2054... Generator Loss: 1.2709
Epoch 3/10... Discriminator Loss: 1.1974... Generator Loss: 0.6812
Epoch 3/10... Discriminator Loss: 1.0313... Generator Loss: 0.8249
Epoch 3/10... Discriminator Loss: 1.3322... Generator Loss: 0.5531
Epoch 3/10... Discriminator Loss: 1.2546... Generator Loss: 0.7545
Epoch 3/10... Discriminator Loss: 1.5219... Generator Loss: 0.3480
Epoch 3/10... Discriminator Loss: 1.4120... Generator Loss: 0.5562
Epoch 3/10... Discriminator Loss: 1.1734... Generator Loss: 0.7465
Epoch 3/10... Discriminator Loss: 1.3460... Generator Loss: 0.5328
Epoch 3/10... Discriminator Loss: 1.5213... Generator Loss: 0.3437
Epoch 3/10... Discriminator Loss: 1.3906... Generator Loss: 0.4361
Epoch 3/10... Discriminator Loss: 1.4224... Generator Loss: 0.6082
Epoch 3/10... Discriminator Loss: 1.3510... Generator Loss: 0.6865
Epoch 4/10... Discriminator Loss: 1.4764... Generator Loss: 0.3552
Epoch 4/10... Discriminator Loss: 1.4188... Generator Loss: 0.6496
Epoch 4/10... Discriminator Loss: 1.1866... Generator Loss: 0.6444
Epoch 4/10... Discriminator Loss: 1.5689... Generator Loss: 1.8186
Epoch 4/10... Discriminator Loss: 1.2511... Generator Loss: 0.7973
Epoch 4/10... Discriminator Loss: 1.4756... Generator Loss: 0.3620
Epoch 4/10... Discriminator Loss: 1.6195... Generator Loss: 0.3380
Epoch 4/10... Discriminator Loss: 1.4466... Generator Loss: 1.4056
Epoch 4/10... Discriminator Loss: 1.3471... Generator Loss: 1.1867
Epoch 4/10... Discriminator Loss: 1.3646... Generator Loss: 0.6244
Epoch 4/10... Discriminator Loss: 1.2908... Generator Loss: 1.0689
Epoch 4/10... Discriminator Loss: 1.3080... Generator Loss: 0.8689
Epoch 4/10... Discriminator Loss: 1.3522... Generator Loss: 0.5157
Epoch 4/10... Discriminator Loss: 1.3028... Generator Loss: 0.6052
Epoch 4/10... Discriminator Loss: 1.3207... Generator Loss: 1.3553
Epoch 4/10... Discriminator Loss: 1.2809... Generator Loss: 0.8412
Epoch 4/10... Discriminator Loss: 1.5559... Generator Loss: 0.3170
Epoch 4/10... Discriminator Loss: 1.2965... Generator Loss: 0.5527
Epoch 4/10... Discriminator Loss: 1.1798... Generator Loss: 0.5923
Epoch 4/10... Discriminator Loss: 1.3635... Generator Loss: 0.6323
Epoch 4/10... Discriminator Loss: 1.2932... Generator Loss: 0.4729
Epoch 4/10... Discriminator Loss: 1.2496... Generator Loss: 0.5682
Epoch 4/10... Discriminator Loss: 1.2354... Generator Loss: 0.6116
Epoch 4/10... Discriminator Loss: 1.8473... Generator Loss: 0.2168
Epoch 4/10... Discriminator Loss: 1.3105... Generator Loss: 0.8098
Epoch 4/10... Discriminator Loss: 1.4958... Generator Loss: 0.3966
Epoch 4/10... Discriminator Loss: 1.2321... Generator Loss: 0.5999
Epoch 4/10... Discriminator Loss: 1.2471... Generator Loss: 0.4849
Epoch 4/10... Discriminator Loss: 1.2572... Generator Loss: 0.8020
Epoch 4/10... Discriminator Loss: 1.2418... Generator Loss: 0.9773
Epoch 4/10... Discriminator Loss: 1.3661... Generator Loss: 0.4990
Epoch 4/10... Discriminator Loss: 1.2220... Generator Loss: 1.0242
Epoch 4/10... Discriminator Loss: 0.9455... Generator Loss: 1.7692
Epoch 4/10... Discriminator Loss: 1.2389... Generator Loss: 1.0294
Epoch 4/10... Discriminator Loss: 1.4850... Generator Loss: 0.3582
Epoch 4/10... Discriminator Loss: 1.3123... Generator Loss: 0.6140
Epoch 4/10... Discriminator Loss: 1.2848... Generator Loss: 0.7380
Epoch 4/10... Discriminator Loss: 1.5624... Generator Loss: 0.3503
Epoch 4/10... Discriminator Loss: 1.3632... Generator Loss: 0.5300
Epoch 4/10... Discriminator Loss: 1.4022... Generator Loss: 0.4743
Epoch 4/10... Discriminator Loss: 1.3978... Generator Loss: 0.4976
Epoch 4/10... Discriminator Loss: 1.1146... Generator Loss: 0.7896
Epoch 4/10... Discriminator Loss: 1.5550... Generator Loss: 0.3160
Epoch 4/10... Discriminator Loss: 1.1679... Generator Loss: 0.7839
Epoch 4/10... Discriminator Loss: 1.3137... Generator Loss: 0.6956
Epoch 4/10... Discriminator Loss: 1.1233... Generator Loss: 0.7922
Epoch 4/10... Discriminator Loss: 1.0628... Generator Loss: 0.8756
Epoch 4/10... Discriminator Loss: 1.4408... Generator Loss: 0.4287
Epoch 4/10... Discriminator Loss: 1.0165... Generator Loss: 1.4519
Epoch 4/10... Discriminator Loss: 1.6210... Generator Loss: 1.3606
Epoch 4/10... Discriminator Loss: 1.3152... Generator Loss: 0.6252
Epoch 4/10... Discriminator Loss: 1.2783... Generator Loss: 0.5280
Epoch 4/10... Discriminator Loss: 1.4192... Generator Loss: 0.4792
Epoch 4/10... Discriminator Loss: 1.3019... Generator Loss: 0.5673
Epoch 4/10... Discriminator Loss: 1.3029... Generator Loss: 0.6722
Epoch 4/10... Discriminator Loss: 1.3772... Generator Loss: 0.4304
Epoch 4/10... Discriminator Loss: 1.2133... Generator Loss: 0.6514
Epoch 4/10... Discriminator Loss: 1.5289... Generator Loss: 0.4073
Epoch 4/10... Discriminator Loss: 1.3886... Generator Loss: 0.7905
Epoch 4/10... Discriminator Loss: 1.3106... Generator Loss: 0.5968
Epoch 4/10... Discriminator Loss: 1.2998... Generator Loss: 0.5820
Epoch 4/10... Discriminator Loss: 1.4201... Generator Loss: 0.5570
Epoch 4/10... Discriminator Loss: 1.4232... Generator Loss: 0.6299
Epoch 4/10... Discriminator Loss: 1.3711... Generator Loss: 1.1340
Epoch 4/10... Discriminator Loss: 1.3839... Generator Loss: 0.4907
Epoch 4/10... Discriminator Loss: 1.2704... Generator Loss: 0.7520
Epoch 4/10... Discriminator Loss: 1.2420... Generator Loss: 0.5469
Epoch 4/10... Discriminator Loss: 1.3504... Generator Loss: 0.9510
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Epoch 4/10... Discriminator Loss: 1.4055... Generator Loss: 1.2061
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Epoch 4/10... Discriminator Loss: 1.3778... Generator Loss: 0.5910
Epoch 4/10... Discriminator Loss: 1.3774... Generator Loss: 0.7819
Epoch 4/10... Discriminator Loss: 1.3047... Generator Loss: 1.1668
Epoch 4/10... Discriminator Loss: 1.2151... Generator Loss: 0.8300
Epoch 4/10... Discriminator Loss: 1.4534... Generator Loss: 0.3863
Epoch 4/10... Discriminator Loss: 1.2418... Generator Loss: 0.6768
Epoch 4/10... Discriminator Loss: 1.3861... Generator Loss: 0.5747
Epoch 4/10... Discriminator Loss: 1.3657... Generator Loss: 1.1567
Epoch 4/10... Discriminator Loss: 1.2964... Generator Loss: 0.7830
Epoch 4/10... Discriminator Loss: 1.1883... Generator Loss: 0.6312
Epoch 4/10... Discriminator Loss: 1.7641... Generator Loss: 0.2549
Epoch 4/10... Discriminator Loss: 1.2923... Generator Loss: 0.6241
Epoch 4/10... Discriminator Loss: 1.2117... Generator Loss: 0.7438
Epoch 4/10... Discriminator Loss: 1.3214... Generator Loss: 1.0731
Epoch 4/10... Discriminator Loss: 1.2260... Generator Loss: 0.9863
Epoch 4/10... Discriminator Loss: 1.5533... Generator Loss: 1.0598
Epoch 4/10... Discriminator Loss: 1.3534... Generator Loss: 0.8282
Epoch 4/10... Discriminator Loss: 1.4415... Generator Loss: 0.4521
Epoch 4/10... Discriminator Loss: 1.2709... Generator Loss: 0.5760
Epoch 4/10... Discriminator Loss: 1.4048... Generator Loss: 0.4671
Epoch 4/10... Discriminator Loss: 1.3900... Generator Loss: 0.6940
Epoch 4/10... Discriminator Loss: 1.0886... Generator Loss: 1.1062
Epoch 4/10... Discriminator Loss: 1.4467... Generator Loss: 0.4576
Epoch 4/10... Discriminator Loss: 1.3034... Generator Loss: 0.5533
Epoch 4/10... Discriminator Loss: 1.4715... Generator Loss: 0.5810
Epoch 4/10... Discriminator Loss: 1.6041... Generator Loss: 0.3071
Epoch 4/10... Discriminator Loss: 1.2059... Generator Loss: 0.6797
Epoch 4/10... Discriminator Loss: 1.3340... Generator Loss: 0.6110
Epoch 4/10... Discriminator Loss: 1.2674... Generator Loss: 1.0403
Epoch 4/10... Discriminator Loss: 1.2035... Generator Loss: 0.8756
Epoch 4/10... Discriminator Loss: 0.6532... Generator Loss: 1.0570
Epoch 4/10... Discriminator Loss: 1.4057... Generator Loss: 1.2640
Epoch 4/10... Discriminator Loss: 1.1242... Generator Loss: 0.8123
Epoch 4/10... Discriminator Loss: 1.3483... Generator Loss: 0.4756
Epoch 4/10... Discriminator Loss: 1.1997... Generator Loss: 1.2999
Epoch 4/10... Discriminator Loss: 1.3970... Generator Loss: 0.4001
Epoch 4/10... Discriminator Loss: 1.7927... Generator Loss: 0.2346
Epoch 4/10... Discriminator Loss: 0.7963... Generator Loss: 0.9539
Epoch 4/10... Discriminator Loss: 0.5448... Generator Loss: 2.7902
Epoch 4/10... Discriminator Loss: 1.1376... Generator Loss: 1.3508
Epoch 4/10... Discriminator Loss: 1.0226... Generator Loss: 0.9370
Epoch 4/10... Discriminator Loss: 1.3131... Generator Loss: 1.2298
Epoch 4/10... Discriminator Loss: 1.3465... Generator Loss: 0.5260
Epoch 4/10... Discriminator Loss: 1.0941... Generator Loss: 1.0290
Epoch 4/10... Discriminator Loss: 1.0842... Generator Loss: 0.6751
Epoch 4/10... Discriminator Loss: 1.5251... Generator Loss: 1.2473
Epoch 4/10... Discriminator Loss: 1.4031... Generator Loss: 0.4376
Epoch 4/10... Discriminator Loss: 1.1503... Generator Loss: 0.9647
Epoch 4/10... Discriminator Loss: 1.6309... Generator Loss: 1.5659
Epoch 4/10... Discriminator Loss: 1.0576... Generator Loss: 0.7149
Epoch 4/10... Discriminator Loss: 1.3282... Generator Loss: 1.7949
Epoch 4/10... Discriminator Loss: 1.2283... Generator Loss: 0.6948
Epoch 4/10... Discriminator Loss: 1.2170... Generator Loss: 0.7694
Epoch 4/10... Discriminator Loss: 1.2077... Generator Loss: 1.0363
Epoch 4/10... Discriminator Loss: 1.6512... Generator Loss: 1.3174
Epoch 4/10... Discriminator Loss: 1.2474... Generator Loss: 0.5512
Epoch 4/10... Discriminator Loss: 1.3055... Generator Loss: 1.2962
Epoch 4/10... Discriminator Loss: 1.2159... Generator Loss: 0.7349
Epoch 4/10... Discriminator Loss: 1.3312... Generator Loss: 0.7235
Epoch 4/10... Discriminator Loss: 1.2804... Generator Loss: 0.5182
Epoch 4/10... Discriminator Loss: 1.2166... Generator Loss: 0.7071
Epoch 4/10... Discriminator Loss: 1.4573... Generator Loss: 0.3912
Epoch 4/10... Discriminator Loss: 1.1345... Generator Loss: 0.8003
Epoch 4/10... Discriminator Loss: 1.3177... Generator Loss: 0.8178
Epoch 4/10... Discriminator Loss: 0.8178... Generator Loss: 1.1333
Epoch 4/10... Discriminator Loss: 1.3084... Generator Loss: 0.9723
Epoch 4/10... Discriminator Loss: 1.4254... Generator Loss: 0.5003
Epoch 4/10... Discriminator Loss: 1.4761... Generator Loss: 0.4203
Epoch 4/10... Discriminator Loss: 1.2755... Generator Loss: 1.0217
Epoch 4/10... Discriminator Loss: 1.3464... Generator Loss: 0.8574
Epoch 4/10... Discriminator Loss: 1.2455... Generator Loss: 0.6467
Epoch 4/10... Discriminator Loss: 1.5105... Generator Loss: 0.4274
Epoch 4/10... Discriminator Loss: 1.4271... Generator Loss: 0.4097
Epoch 4/10... Discriminator Loss: 1.3021... Generator Loss: 0.7584
Epoch 4/10... Discriminator Loss: 1.3311... Generator Loss: 0.6789
Epoch 4/10... Discriminator Loss: 1.2052... Generator Loss: 0.5443
Epoch 4/10... Discriminator Loss: 1.3120... Generator Loss: 1.1198
Epoch 4/10... Discriminator Loss: 1.2551... Generator Loss: 0.6468
Epoch 4/10... Discriminator Loss: 1.2860... Generator Loss: 0.4976
Epoch 4/10... Discriminator Loss: 1.1826... Generator Loss: 0.6669
Epoch 4/10... Discriminator Loss: 1.3108... Generator Loss: 0.6162
Epoch 4/10... Discriminator Loss: 1.2251... Generator Loss: 0.7358
Epoch 4/10... Discriminator Loss: 1.3655... Generator Loss: 0.5173
Epoch 4/10... Discriminator Loss: 1.4279... Generator Loss: 0.7712
Epoch 4/10... Discriminator Loss: 1.3004... Generator Loss: 0.6671
Epoch 4/10... Discriminator Loss: 1.2795... Generator Loss: 0.7348
Epoch 5/10... Discriminator Loss: 1.2158... Generator Loss: 0.7349
Epoch 5/10... Discriminator Loss: 1.6003... Generator Loss: 0.3051
Epoch 5/10... Discriminator Loss: 1.3091... Generator Loss: 0.7006
Epoch 5/10... Discriminator Loss: 1.1961... Generator Loss: 0.7269
Epoch 5/10... Discriminator Loss: 1.3720... Generator Loss: 0.4971
Epoch 5/10... Discriminator Loss: 1.2482... Generator Loss: 0.6883
Epoch 5/10... Discriminator Loss: 1.4721... Generator Loss: 0.5205
Epoch 5/10... Discriminator Loss: 1.4826... Generator Loss: 1.1839
Epoch 5/10... Discriminator Loss: 1.0833... Generator Loss: 1.3601
Epoch 5/10... Discriminator Loss: 1.2637... Generator Loss: 0.6245
Epoch 5/10... Discriminator Loss: 1.2369... Generator Loss: 0.7958
Epoch 5/10... Discriminator Loss: 1.2253... Generator Loss: 0.9068
Epoch 5/10... Discriminator Loss: 1.2493... Generator Loss: 1.4409
Epoch 5/10... Discriminator Loss: 1.4209... Generator Loss: 0.4586
Epoch 5/10... Discriminator Loss: 1.2401... Generator Loss: 0.5985
Epoch 5/10... Discriminator Loss: 1.3984... Generator Loss: 0.8479
Epoch 5/10... Discriminator Loss: 1.3391... Generator Loss: 0.5201
Epoch 5/10... Discriminator Loss: 1.4186... Generator Loss: 0.4918
Epoch 5/10... Discriminator Loss: 1.3046... Generator Loss: 0.8815
Epoch 5/10... Discriminator Loss: 1.2346... Generator Loss: 0.8735
Epoch 5/10... Discriminator Loss: 1.2106... Generator Loss: 0.7450
Epoch 5/10... Discriminator Loss: 1.3202... Generator Loss: 0.4850
Epoch 5/10... Discriminator Loss: 1.3438... Generator Loss: 0.4631
Epoch 5/10... Discriminator Loss: 1.2930... Generator Loss: 0.7228
Epoch 5/10... Discriminator Loss: 1.0161... Generator Loss: 1.0007
Epoch 5/10... Discriminator Loss: 1.0689... Generator Loss: 0.7847
Epoch 5/10... Discriminator Loss: 1.2145... Generator Loss: 0.8504
Epoch 5/10... Discriminator Loss: 1.3101... Generator Loss: 0.5717
Epoch 5/10... Discriminator Loss: 1.2293... Generator Loss: 0.7917
Epoch 5/10... Discriminator Loss: 1.2577... Generator Loss: 0.5087
Epoch 5/10... Discriminator Loss: 1.2833... Generator Loss: 0.6402
Epoch 5/10... Discriminator Loss: 1.1660... Generator Loss: 0.8327
Epoch 5/10... Discriminator Loss: 1.1886... Generator Loss: 0.6344
Epoch 5/10... Discriminator Loss: 1.3480... Generator Loss: 0.8986
Epoch 5/10... Discriminator Loss: 1.3370... Generator Loss: 0.5365
Epoch 5/10... Discriminator Loss: 1.2849... Generator Loss: 0.6217
Epoch 5/10... Discriminator Loss: 1.3922... Generator Loss: 0.5601
Epoch 5/10... Discriminator Loss: 1.3969... Generator Loss: 0.4301
Epoch 5/10... Discriminator Loss: 1.3716... Generator Loss: 0.7560
Epoch 5/10... Discriminator Loss: 1.3376... Generator Loss: 0.8085
Epoch 5/10... Discriminator Loss: 1.2843... Generator Loss: 0.6795
Epoch 5/10... Discriminator Loss: 1.2255... Generator Loss: 0.8108
Epoch 5/10... Discriminator Loss: 1.5061... Generator Loss: 0.5799
Epoch 5/10... Discriminator Loss: 1.5269... Generator Loss: 0.4014
Epoch 5/10... Discriminator Loss: 1.2673... Generator Loss: 0.6504
Epoch 5/10... Discriminator Loss: 1.4377... Generator Loss: 0.7310
Epoch 5/10... Discriminator Loss: 1.2881... Generator Loss: 0.9979
Epoch 5/10... Discriminator Loss: 1.3936... Generator Loss: 0.5203
Epoch 5/10... Discriminator Loss: 1.5120... Generator Loss: 1.0150
Epoch 5/10... Discriminator Loss: 1.3509... Generator Loss: 0.5906
Epoch 5/10... Discriminator Loss: 1.3871... Generator Loss: 0.5452
Epoch 5/10... Discriminator Loss: 1.3013... Generator Loss: 0.6628
Epoch 5/10... Discriminator Loss: 1.2695... Generator Loss: 0.5602
Epoch 5/10... Discriminator Loss: 1.7289... Generator Loss: 0.2461
Epoch 5/10... Discriminator Loss: 1.2859... Generator Loss: 1.0427
Epoch 5/10... Discriminator Loss: 1.3661... Generator Loss: 0.5512
Epoch 5/10... Discriminator Loss: 1.3390... Generator Loss: 0.7412
Epoch 5/10... Discriminator Loss: 1.3081... Generator Loss: 0.6619
Epoch 5/10... Discriminator Loss: 1.3001... Generator Loss: 0.6433
Epoch 5/10... Discriminator Loss: 1.3809... Generator Loss: 0.7999
Epoch 5/10... Discriminator Loss: 1.1542... Generator Loss: 0.6733
Epoch 5/10... Discriminator Loss: 1.2334... Generator Loss: 0.7797
Epoch 5/10... Discriminator Loss: 1.2716... Generator Loss: 0.5827
Epoch 5/10... Discriminator Loss: 1.1849... Generator Loss: 0.8105
Epoch 5/10... Discriminator Loss: 1.1346... Generator Loss: 0.9415
Epoch 5/10... Discriminator Loss: 1.2311... Generator Loss: 0.9305
Epoch 5/10... Discriminator Loss: 1.3248... Generator Loss: 0.8781
Epoch 5/10... Discriminator Loss: 1.2688... Generator Loss: 0.8066
Epoch 5/10... Discriminator Loss: 1.3860... Generator Loss: 0.5817
Epoch 5/10... Discriminator Loss: 1.1851... Generator Loss: 0.7597
Epoch 5/10... Discriminator Loss: 1.2350... Generator Loss: 1.3199
Epoch 5/10... Discriminator Loss: 1.3437... Generator Loss: 0.5637
Epoch 5/10... Discriminator Loss: 1.3492... Generator Loss: 0.6224
Epoch 5/10... Discriminator Loss: 1.2276... Generator Loss: 0.7721
---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-12-8541c3781947> in <module>()
     13 with tf.Graph().as_default():
     14     train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
---> 15           celeba_dataset.shape, celeba_dataset.image_mode)

<ipython-input-11-65a53f333d90> in train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode)
     45                 if steps % 10 == 0:
     46                     # At the end of each epoch, get the losses and print them out
---> 47                     train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
     48                     train_loss_g = g_loss.eval({input_z: batch_z})
     49 

/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in eval(self, feed_dict, session)
    579 
    580     """
--> 581     return _eval_using_default_session(self, feed_dict, self.graph, session)
    582 
    583 

/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/framework/ops.py in _eval_using_default_session(tensors, feed_dict, graph, session)
   3795                        "the tensor's graph is different from the session's "
   3796                        "graph.")
-> 3797   return session.run(tensors, feed_dict)
   3798 
   3799 

/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
    765     try:
    766       result = self._run(None, fetches, feed_dict, options_ptr,
--> 767                          run_metadata_ptr)
    768       if run_metadata:
    769         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
    963     if final_fetches or final_targets:
    964       results = self._do_run(handle, final_targets, final_fetches,
--> 965                              feed_dict_string, options, run_metadata)
    966     else:
    967       results = []

/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
   1013     if handle is None:
   1014       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
-> 1015                            target_list, options, run_metadata)
   1016     else:
   1017       return self._do_call(_prun_fn, self._session, handle, feed_dict,

/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
   1020   def _do_call(self, fn, *args):
   1021     try:
-> 1022       return fn(*args)
   1023     except errors.OpError as e:
   1024       message = compat.as_text(e.message)

/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata)
   1002         return tf_session.TF_Run(session, options,
   1003                                  feed_dict, fetch_list, target_list,
-> 1004                                  status, run_metadata)
   1005 
   1006     def _prun_fn(session, handle, feed_dict, fetch_list):

KeyboardInterrupt: 

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.